AWS ML, AI, & Data Analytics

Question 1

What is Machine Learning (ML)?

Answer 1

Technology that trains algorithms with data to make predictions/decisions without being explicitly programmed.

Question 2

What is Artificial Intelligence (AI)?

Answer 2

Broader field simulating human-like cognitive abilities (learning, reasoning, recognition). ML is a subset of AI.

Question 3

Name 4 types of ML data sets

Answer 3

Supervised (labeled data), Unsupervised (find patterns in unlabeled data), Semi-supervised (mix of both), Reinforcement learning (trial-error with rewards).

Question 4

Difference AI vs ML (exam level)?

Answer 4

AI = goal of simulating human intelligence. ML = practical approach using algorithms/data to achieve that intelligence.

Question 5

AWS service for building, training, and deploying ML models?

Answer 5

Amazon SageMaker

Question 6

Use cases of SageMaker

Answer 6

Fraud detection, churn prediction, personalized recommendations, document analysis.

Question 7

Which AWS AI service builds chatbots/voice assistants?

Answer 7

Amazon Lex

Question 8

Core concept of Lex (exam tip)?

Answer 8

Conversational interface → intents, utterances, slots, fulfillment.

Question 9

AWS AI service for enterprise search with NLP and LLM integration?

Answer 9

Amazon Kendra

Question 10

Use cases of Kendra

Answer 10

Intelligent search across documents (S3, Salesforce, Slack), FAQs, employee productivity, self-service bots.

Question 11

AWS service to run SQL queries on S3 data lakes?

Answer 11

Amazon Athena

Question 12

Athena is…

Answer 12

Serverless, interactive analytics tool (analyze data in S3 with SQL/Python).

Question 13

Common use case of Athena

Answer 13

Query raw data in S3 without ETL, ad-hoc analytics, BI queries.

Question 14

AWS service for real-time streaming analytics?

Answer 14

Amazon Kinesis

Question 15

Kinesis products (exam tip)

Answer 15

1. Video Streams (stream video)
2. Data Streams (real-time data capture)
3. Firehose (load into AWS stores like S3/Redshift)
4. Data Analytics (analyze streams in SQL/Flink).

Question 16

AWS service for ETL (Extract, Transform, Load)?

Answer 16

AWS Glue

Question 17

What is AWS Glue used for?

Answer 17

Discover, prepare, and integrate data across sources; build data catalog; run serverless ETL jobs into data lakes/warehouses.

Question 18

Glue + Athena relationship

Answer 18

Glue catalogs & prepares data → Athena queries it serverlessly with SQL.

Question 19

AWS service for Business Intelligence (BI) dashboards?

Answer 19

Amazon QuickSight

Question 20

Benefits of QuickSight

Answer 20

Interactive dashboards, paginated reports, embedded analytics, NLQ (“Q”), scales to 1000s of users, pay-per-session.

Question 21

AWS service for Big Data frameworks like Spark, Hive, Presto?

Answer 21

Amazon EMR (Elastic MapReduce)

Question 22

EMR is used for…

Answer 22

Processing petabytes of data, big data analytics, data pipelines, ML preprocessing using open-source frameworks.

Question 23

EMR vs SageMaker (exam difference)

Answer 23

EMR = process big data at scale (data engineering). SageMaker = train & deploy ML models (data science).